1,695 research outputs found
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
Sensing-Throughput Tradeoff for Superior Selective Reporting-based Spectrum Sensing in Energy Harvesting HCRNs
In this paper, we investigate the performance of conventional cooperative
sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing
in an energy harvesting-enabled heterogeneous cognitive radio network (HCRN).
In particular, we derive expressions for the achievable throughput of both
schemes and formulate nonlinear integer programming problems, in order to find
the throughput-optimal set of spectrum sensors scheduled to sense a particular
channel, given primary user (PU) interference and energy harvesting
constraints. Furthermore, we present novel solutions for the underlying
optimization problems based on the cross-entropy (CE) method, and compare the
performance with exhaustive search and greedy algorithms. Finally, we discuss
the tradeoff between the average achievable throughput of the SSR and CCS
schemes, and highlight the regime where the SSR scheme outperforms the CCS
scheme. Notably, we show that there is an inherent tradeoff between the channel
available time and the detection accuracy. Our numerical results show that, as
the number of spectrum sensors increases, the channel available time gains a
higher priority in an HCRN, as opposed to detection accuracy
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
End-to-end Throughput Maximization for Underlay Multi-hop Cognitive Radio Networks with RF Energy Harvesting
This paper studies a green paradigm for the underlay coexistence of primary
users (PUs) and secondary users (SUs) in energy harvesting cognitive radio
networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the
energy of PUs to enhance spectrum efficiency and green energy utilization. To
lower the transmit powers of SUs, we employ multi-hop transmission with time
division multiple access, by which SUs first harvest energy from the RF signals
of PUs and then transmit data in the allocated time concurrently with PUs, all
in the licensed spectrum. In this way, the available transmit energy of each SU
mainly depends on the harvested energy before the turn to transmit, namely
energy causality. Meanwhile, the transmit powers of SUs must be strictly
controlled to protect PUs from harmful interference. Thus, subject to the
energy causality constraint and the interference power constraint, we study the
end-to-end throughput maximization problem for optimal time and power
allocation. To solve this nonconvex problem, we first equivalently transform it
into a convex optimization problem and then propose the joint optimal time and
power allocation (JOTPA) algorithm that iteratively solves a series of
feasibility problems until convergence. Extensive simulations evaluate the
performance of EH-CRNs with JOTPA in three typical deployment scenarios and
validate the superiority of JOTPA by making comparisons with two other resource
allocation algorithms
FreeNet: Spectrum and Energy Harvesting Wireless Networks
The dramatic mobile data traffic growth is not only resulting in the spectrum
crunch but is also leading to exorbitant energy consumption. It is thus
desirable to liberate mobile and wireless networks from the constraint of the
spectrum scarcity and to rein in the growing energy consumption. This article
introduces FreeNet, figuratively synonymous to "Free Network", which engineers
the spectrum and energy harvesting techniques to alleviate the spectrum and
energy constraints by sensing and harvesting spare spectrum for data
communications and utilizing renewable energy as power supplies, respectively.
Hence, FreeNet increases the spectrum and energy efficiency of wireless
networks and enhances the network availability. As a result, FreeNet can be
deployed to alleviate network congestion in urban areas, provision broadband
services in rural areas, and upgrade emergency communication capacity. This
article provides a brief analysis of the design of FreeNet that accommodates
the dynamics of the spare spectrum and employs renewable energy
Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer
In this paper, we study resource allocation for multiuser multiple-input
single-output secondary communication systems with multiple system design
objectives. We consider cognitive radio networks where the secondary receivers
are able to harvest energy from the radio frequency when they are idle. The
secondary system provides simultaneous wireless power and secure information
transfer to the secondary receivers. We propose a multi-objective optimization
framework for the design of a Pareto optimal resource allocation algorithm
based on the weighted Tchebycheff approach. In particular, the algorithm design
incorporates three important system objectives: total transmit power
minimization, energy harvesting efficiency maximization, and interference power
leakage-to-transmit power ratio minimization. The proposed framework takes into
account a quality of service requirement regarding communication secrecy in the
secondary system and the imperfection of the channel state information of
potential eavesdroppers (idle secondary receivers and primary receivers) at the
secondary transmitter. The adopted multi-objective optimization problem is
non-convex and is recast as a convex optimization problem via semidefinite
programming (SDP) relaxation. It is shown that the global optimal solution of
the original problem can be constructed by exploiting both the primal and the
dual optimal solutions of the SDP relaxed problem. Besides, two suboptimal
resource allocation schemes for the case when the solution of the dual problem
is unavailable for constructing the optimal solution are proposed. Numerical
results not only demonstrate the close-to-optimal performance of the proposed
suboptimal schemes, but also unveil an interesting trade-off between the
considered conflicting system design objectives.Comment: Accepted with minor revisions for publication as a regular paper in
the IEEE Transactions on Vehicular Technolog
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer
Radio frequency (RF) energy harvesting and transfer techniques have recently
become alternative methods to power the next generation of wireless networks.
As this emerging technology enables proactive replenishment of wireless
devices, it is advantageous in supporting applications with quality-of-service
(QoS) requirement. This article focuses on the resource allocation issues in
wireless networks with RF energy harvesting capability, referred to as RF
energy harvesting networks (RF-EHNs). First, we present an overview of the
RF-EHNs, followed by a review of a variety of issues regarding resource
allocation. Then, we present a case study of designing in the receiver
operation policy, which is of paramount importance in the RF-EHNs. We focus on
QoS support and service differentiation, which have not been addressed by
previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ
Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
In this paper, we study the transmission strategy adaptation problem in an
RF-powered cognitive radio network, in which hybrid secondary users are able to
switch between the harvest-then-transmit mode and the ambient backscatter mode
for their communication with the secondary gateway. In the network, a monetary
incentive is introduced for managing the interference caused by the secondary
transmission with imperfect channel sensing. The sensing-pricing-transmitting
process of the secondary gateway and the transmitters is modeled as a
single-leader-multi-follower Stackelberg game. Furthermore, the follower
sub-game among the secondary transmitters is modeled as a generalized Nash
equilibrium problem with shared constraints. Based on our theoretical
discoveries regarding the properties of equilibria in the follower sub-game and
the Stackelberg game, we propose a distributed, iterative strategy searching
scheme that guarantees the convergence to the Stackelberg equilibrium. The
numerical simulations show that the proposed hybrid transmission scheme always
outperforms the schemes with fixed transmission modes. Furthermore, the
simulations reveal that the adopted hybrid scheme is able to achieve a higher
throughput than the sum of the throughput obtained from the schemes with fixed
transmission modes
Energy Efficient Resource Allocation in EH-enabled CR Networks for IoT
With the rapid growth of Internet of Things (IoT) devices, the next
generation mobile networks demand for more operating frequency bands. By
leveraging the underutilized radio spectrum, the cognitive radio (CR)
technology is considered as a promising solution for spectrum scarcity problem
of IoT applications. In parallel with the development of CR techniques,
Wireless Energy Harvesting (WEH) is considered as one of the emerging
technologies to eliminate the need of recharging or replacing the batteries for
IoT and CR networks. To this end, we propose to utilize WEH for CR networks in
which the CR devices are not only capable of sensing the available radio
frequencies in a collaborative manner but also harvesting the wireless energy
transferred by an Access Point (AP). More importantly, we design an
optimization framework that captures a fundamental tradeoff between energy
efficiency (EE) and spectral efficiency (SE) of the network. In particular, we
formulate a Mixed Integer Nonlinear Programming (MINLP) problem that maximizes
EE while taking into consideration of users' buffer occupancy, data rate
fairness, energy causality constraints and interference constraints. We further
prove that the proposed optimization framework is an NP-Hard problem. Thus, we
propose a low complex heuristic algorithm, called INSTANT, to solve the
resource allocation and energy harvesting optimization problem. The proposed
algorithm is shown to be capable of achieving near optimal solution with high
accuracy while having polynomial complexity. The efficiency of our proposal is
validated through well designed simulations
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